To address the issue of thermal energy supply and demand balance during deep peak regulation of thermal power units with a high proportion of new energy, this paper proposes an automated regulation algorithm that integrates reinforcement learning and adaptive control. Using the Markov decision process as a framework, a high dimensional state space and a multi-constrained action space are constructed, and a multi-objective weighted reward function is designed to balance accuracy, speed and economy. A 600 MW unit simulation platform is built based on MATLAB/SIMULINK and verified under conditions of rapid load rise and fall, low load stability, and variable operating disturbances. The results show that: when the load fluctuates, the main steam pressure overshoot is 2.1%, the adjustment time is 320 seconds, and IAE and ISE are 42.3% and 58.7% lower than PID, respectively; the temperature fluctuation in low-load operation is ?0.5%, and the coal consumption is 322 g/kWh, which is 4.7% lower than PID. The recovery time in the anti-disturbance experiment is 28 seconds, and the maximum deviation is 0.4 MPa. The robustness is superior to that of the traditional algorithm, providing technical support for deep peak regulation.
Wang et al. (Wed,) studied this question.